Metabolic features’ effect on FibroScan-AST (FAST) score in Egyptian patients with metabolic dysfunction-associated steatotic liver disease (MASLD) (original) (raw)

Abstract

Background

Metabolic dysfunction-associated steatotic liver disease (MASLD) is rising these days together with type 2 diabetes (T2DM) and obesity levels, which is the leading cause of chronic liver disease globally. Noninvasive markers can be used to detect patients with severe fibrosis and active MASH. FibroScan-AST (FAST) score is a simplified combination score that accounts for controlled attenuation parameter (CAP), liver stiffness (LSM), and serum aspartate aminotransferase (AST). It has been utilized to recognize individuals with fibrotic metabolic associated steatohepatitis (MASH). This study's goal is to figure out the metabolic features linked to a high FAST score.

Methods

a cross-sectional study that involved 385 participants with MASLD collected from Fibroscan Unit, steatosis detected by VCTE-CAP elastography. Then, the FAST score was estimated sorting patients according to their risk of developing fibrotic MASH as low (< 0.35), medium (0.35–0.67), or high (> 0.67).

Results

Linear regression identified the cumulative number of metabolic criteria as a significant predictor of higher FAST score (B = 0.107, p < 0.001). In multivariable logistic regression, T2DM (OR = 3.62, 95% CI: 1.65–7.96, p = 0.001), hypertension (OR = 3.20, 95% CI: 1.45–7.07, p = 0.004), and dyslipidemia (OR = 3.28, 95% CI: 1.41–7.65, p = 0.006) were each independently associated with a high-risk FAST score. Age, sex, and body mass index were not significant predictors in the adjusted models.

Conclusions

calculating FAST score is crucial in identifying patients with fibrotic MASH. T2DM, Dyslipidemia, hypertension, and the presence of all cardiometabolic criteria have a substantially elevated risk of fibrosis and steatohepatitis.

Trial registration

NCT06867419

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Introduction

Macrovesicular steatosis in ≥ 5% hepatocytes, without any other etiologies like drugs or alcohol, is the hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD). Which covers a wide range of illnesses from metabolic dysfunction-associated steatotic liver (MASL) through to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis and cirrhosis [1]. The morbidity and mortality increases with the advancement of liver fibrosis. MASLD patients with significant and advanced fibrosis (≥ F2) and nonalcoholic fatty liver disease (NAFLD) activity score (NAS) ≥ 4 are referred to as at risk MASH. These patients are the focus of clinical trials as they are the target of pharmacological therapies e.g. resmetirom [2, 3].

According to estimates, MASLD affects 30% of adults globally, and between 1991 and 2019, its prevalence rose from 22 to 37%. The rising rates of obesity and disorders linked to obesity coincide with the rising rates of MASLD [4, 5].

The metabolic syndrome is closely linked to the onset of MASLD. About 90% of individuals with MASLD have more than one metabolic syndrome feature, and roughly 33% have three or more criteria [6]. Furthermore, the more severe the obesity, the higher the chance of getting MASH. Regardless of body mass index, there is a uniform correlation between MASH and insulin resistance, indicating that an essential component in the pathogenesis of MASH is insulin resistance [7, 8].

At the moment, the most reliable method for diagnosing MASLD and histological evaluation is believed to be liver biopsy. Unfortunately, a biopsy cannot be used early in a potential patient's diagnostic journey and is not appropriate for screening due to its invasive nature. It is primarily used to differentiate MASH from MASL, determine the extent of liver fibrosis, and determine individuals who are most susceptible to progressive liver disease over an extended period of time [9].

Despite being the top standard, a liver biopsy has a number of drawbacks. There are various dangers and discomfort associated with the operation. According to reports, the incidence of pain was 20%. Additionally, the evaluation of features, categorization, and numerical scoring varies both within and across observers. [10] Also, sampling error results from the uneven distribution of fibrosis across the liver [11].

Some of the drawbacks of the biopsy can be addressed by non-intrusive evaluation of liver fibrosis, which can be applied and utilized for MASLD screening. In MASLD, a thorough search is presently underway for biomarkers [12]. One of these important characteristics is liver fibrosis, as previously mentioned, and non-intrusive evaluation of liver fibrosis has advanced significantly during the past 20 years. There are now two distinct methods for non-intrusive assessment of liver fibrosis: a biological method based on measuring biomarkers, primarily in blood, and a physical method based on measuring liver stiffness [9].

Numerous noninvasive tests (NITs) have been researched and validated with a moderate predictive potential to accurately eliminate advanced fibrosis. These tests include radiological-based tests like vibration-controlled transient elastography (VCTE) and magnetic resonance elastography, as well as serum-based tests like the NAFLD fibrosis score (NFS), fibrosis-4 index (FIB-4), and aspartate aminotransferase (AST)/platelet ratio index (APRI) [13].

FibroScan liver stiffness measurement (LSM) and controlled attenuation parameter (CAP) are non-intrusive techniques that can diagnose liver fibrosis and steatosis in MASLD and MASH, respectively. FibroScan-AST (FAST), which integrates amino aspartate transferase (AST) testing with FibroScan LSM and CAP measurement, enables the diagnosis of at-risk NASH patients [14].

Data from seven clinical studies in Europe, North America, and Asia were used to validate the FAST score, which was created using information from patients who underwent liver biopsies at several liver centers in England for MASLD. By interpretation of the FAST score results, the chance of at-risk NASH was high for patients with a score ≥ 0.67, moderate for those with a score between 0.35 and < 0.67, and low for those with a score < 0.35. There is correlation between FAST score cut off value of high risk group and significant liver fibrosis (≥ F2), and elevated NAFLD activity score (NAS ≥ 4), known as at risk MASH [15].

In our research we monitored the influence of the metabolic syndrome’s features on stratifying FAST score results in MASLD patients in Egypt.

Patients and methods

We conducted a cross-sectional study on 385 MASLD patients. The patients were gathered from Tanta University’s Fibroscan Unit in the Tropical Medicine and Infectious Disease Department between September 2024 and March 2025.

Tanta University’s local ethics committee for the medical faculty gave its approval to this study, approval code: 36264PR889/10/24. The study was registered on clinicaltrial.gov with NCT number of NCT06867419. A written informed consent form was filled out by each patient.

We included all adult patients with MASLD who had steatosis detected by abdominal ultrasound, VCTE-CAP elastography, and noninvasive biomarkers and scores with association of one of the following 5 components: overweight/obesity (body mass index (BMI) ≥ 25 kg/m2 or waist circumference (WC) ≥ 94 cm in males and ≥ 80 cm in females), hypertension (≥ 130/85 mm Hg or medications for hypertension), T2DM (≥ 100 mg/dL fasting sugar or glycated hemoglobin (HbA1c) ≥ 5.7% or medications for elevated glucose) and Dyslipidemia [either triglyceride level ≥ 150 mg/dL or reduced values of high-density lipoprotein cholesterol (HDL) (≤ 40 mg/dL for males ≤ 50 mg/dL for females or drug treatment to lower lipids) [16].

Patients having a history of heavy drinking (> 30 gm/day for males and more than 20 gm/day for females) and positive viral markers for hepatitis B or C, suffering from autoimmune hepatitis, presence of other metabolic liver disease or having hepatic focal lesions were excluded [17, 18].

The patients who were eligible for this study underwent full clinical examination, detailed history taking, anthropometric measurements [WC, mid arm circumference (MAC), BMI]. Liver profile, lipid profile, renal function tests, virological markers, HbA1c, fasting blood sugar, 2 h post prandial blood sugar, fasting insulin and Homeostatic model assessment of Insulin Resistance (HOMA- IR) test were investigated. APRI [(AST level/Upper Limit of Normal)/platelet count (109/l) × 100], NAFLD fibrosis score (NFS) [–1.675 + 0.037 – age (years) + 0.094 – BMI (kg/m2) + 1.13 × IFG/diabetes (yes = 1, no = 0) + 0.99 × AST/ALT ratio – 0.013 × platelet count (× 109/l) – 0.66 × albumin (g/dl)], and FIB-4 [Age (years) × AST(U/L)/[platelet count (109/L) × ALT1/2 (U/L)] have been measured [19,20,21].

Examination of liver stiffness and steatosis has been achieved by the FibroScan® echosens model 502 V2 Touch. Firstly, three hours of fasting was advised for patients preceding the procedure. We used M or XL probes to take the VCTE measures. The M probe was used initially for every inspection, but the operator might switch to the XL probe if necessary in accordance with the device's recommendations and instructions. At least ten valid measurements with an interquartile range below 30% were acquired by the operator and over 60% each participant's success rate and the machine determined the LSM and CAP median values [22].

CAP values classified steatosis into Mild (S1) at 234dB/m, moderate (S2) at 270 dB/m, and severe (S3) at 301 dB/m. A LSM thresholds of 7.2, 9.6, and 14.5 kPa was set to recognize significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (F4), respectively [23, 24].

Then, the following components were used to calculate FAST score: CAP and LSM from VCTE examination and AST level in its calculation. This formula can be used for calculation: [e (- 1.65 + 1.07 × In (LSM) + 2.66*10–8 × CAP3 −63.3 × AST-1)]/[1 + e (−1.65 + 1.07 × In (LSM) + 2.66*10 8 × CAP3 −63.3 × AST-1)]. In addition, the FAST score can be easily calculated using a free application called my FibroScan (Echosens, Paris, France), and the algorithm is publicly available [15].

FAST score identifies MASLD patients with high risk of disease progression to fibrotic MASH. According to the result of FAST score the patients were subdivided into 3 groups:

Statistical analysis

The sample size was calculated based on MASLD prevalence in Egypt of 47.5% with 95% confidence and 95% confidence and ± 5% precision [25]. The minimum required sample is 384 participants.

The evaluation of data was conducted using IBM SPSS 22, which is designed for Microsoft Windows (Armonk, NY). The mean and standard deviation were computed for the numerical parameter. For median comparisons, Kruskal–Wallis testwas computed, afterwards conducting post-hoc pairwise Mann–Whitney U tests with Bonferroni correction. Numbers and percentages were generated for categorical variables, and the chi-square test or the Fisher-Freeman-Halton exact test with Monte Carlo simulation was executed to examine the connection among the categorical values. For statistical significance, P values < 0.05 were adopted. The impact cardiometabolic components to detect disease severity was evaluated using multiple linear regression. Two models were constructed: a model using the count of metabolic criteria, and a full model using the individual metabolic components (T2DM, hypertension, dyslipidemia, BMI), both adjusted for age and sex. Subsequently, binary logistic regression was used with the dichotomous outcome of High-Risk FAST score (> 0.67) to identify factors associated with the clinically critical threshold.

Results

Four hundred MASLD patients visited the unit, 9 patients were disqualified due to a history of hepatitis B and C infections, 3 patients with hepatic focal lesions, 1 autoimmune hepatitis patient and 2 patients due to failed VCTE reading. So, in this study, 385 patients were included.

Our patients were 47.33 ± 12.43 years old on average, and 210 (54.5%) of them were men. Among the cardiometabolic criteria, obesity was most frequently observed in 301 patients (78.2%), preceding dyslipidemia in 261 patients (67.8%), type 2 diabetes in 147 patients (38.2%), and hypertension in 131 patients (34%). Of the patients, 42 (10.9%) met all four cardiometabolic criteria. (Table 1).

Table 1 Comparison of basal patients’ characteristics based on FAST score classification

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The FAST score mean was 0.27 ± 0.23. Three groups of patients were created based on the FAST score classification: [1] There were 258 (67.01%) patients in the low-risk MASH group whose FAST score was below 0.35. [2] 93 (24.16%) individuals with FAST scores between 0.35 and 0.67 were in the medium-risk MASH group. [3] The high-risk MASH group comprised 34 patients (8.83%) with a FAST score of over 0.67. (Table 1).

Comparison of the three groups’ clinical, biochemical, and Fibroscan values: (Table 1)

Comparing age, sex, BMI, and waist circumference among the 3 groups revealed insignificant differences between the researched groups.

Among the cardiometabolic criteria, the presence of hypertension, T2DM, dyslipidemia, and 4 metabolic criteria (P < 0.05) were significantly different across groups, while obesity demonstrated insignificant difference (P = 0.512).

Fibroscan examination showed that both LSM and CAP were significantly elevated in high-risk group opposed to the other 2 groups (P < 0.001). Out of our 385 patients, fibrosis stage F0 was found in 272 (70.6%), F1 in 30 (7.8%), F2 in 35 (9.1%), F3 in 32 (8.3%), and F4 in 16 (4.2%) patients.

While the complete blood count parameters showed insignificant difference across the 3 groups, the hepatic function tests were significantly different. In contrast to low-risk group, total bilirubin was significantly elevated in high-risk group (P = 0.003). ALT, AST, and INR were significantly elevated in high-risk group (P < 0.001), yet serum albumin was significantly reduced in high-risk group than the other groups (P < 0.001).

Regarding the lipid profile, serum cholesterol was insignificantly different across the groups. Serum triglycerides was significantly elevated, while HDL cholesterol was significantly reduced in medium and high-risk groups, compared to low-risk patients (P < 0.001). LDL cholesterol was significantly higher in high-risk group contrary to the low -risk group (P = 0.032).

In comparison to low-risk group, fasting sugar, 2 h post prandial sugar, and HbA1c were significantly elevated in high-risk group compared to low risk group (P < 0.05). HOMA-IR revealed significant difference between the low and high risk groups (P = 0.009).

The NFS, FIB-4, and APRI scores were significantly higher in high-risk group in contrast to other groups (P < 0.001).

Regression for predicting cardiometabolic criteria’s effect on FAST score stratification: (Tables 2 & 3)

Table 2 Associations of metabolic factors with the FAST score in MASLD patients (n = 385): results from multiple linear regression analysis

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Table 3 Multivariable logistic regression analysis of factors associated with a high-risk FAST score (> 0.67) in MASLD patients (n = 385)

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Multiple linear regression was done to evaluate the effect of the cardiometabolic criteria on predicting the severity of liver disease represented by higher FAST score. Two models were constructed to explore the composite burden of all cardiometabolic components and the burden of each individual component on predicting at risk MASH. (Table 2).

The first model tested the composite cardiometabolic burden, the presence of all four metabolic criteria was a highly significant predictor of higher FAST score (B = 0.107, 95% CI: 0.081—0.133, p < 0.001), indicating that each additional cardiometabolic criterion was associated with a 0.107-point increase in the FAST score. (Table 2).

The second model deconstructed this metabolic burden into its core components. This model revealed that T2DM (B = 0.096, 95% CI: 0.05—0.142, p < 0.001), hypertension (B = 0.066, 95% CI: 0.018—0.114, _p_ = 0.007), and dyslipidemia (B = 0.120, 95% CI: 0.076—0.164, _p_ < 0.001) were each independently and significantly associated with a higher FAST score when mutually adjusted. The similar magnitude of their standardized effects (Beta coefficients of 0.202, 0.135, and 0.259, respectively) suggests comparable contributions from each condition to the overall metabolic risk. Notably, age, sex, and body mass index were not significant predictors in either multivariate model (all _p_ > 0.05). (Table 2).

In multivariable logistic regression analysis, type 2 diabetes mellitus (OR = 3.62; 95% CI: 1.65–7.96; p = 0.001), hypertension (OR = 3.20; 95% CI: 1.45–7.07; p = 0.004), and dyslipidemia (OR = 3.28; 95% CI: 1.41–7.65; p = 0.006) were independent predictors of a high-risk FAST score (> 0.67). T2DM, hypertension, and dyslipidemia, was each associated with an approximate three-fold increase in odds of having at risk MASH (Nagelkerke R2 = 0.174). (Table 3).

T2DM was the primary metabolic predictor of at high-risk FAST score having the highest odds ratio, the confidence intervals overlapped considerably, indicating comparable effects. Consistent with the linear regression findings, age, sex, and BMI were not significant predictors in this adjusted model (all p > 0.05). (Table 3).

Discussion

MASLD is characterized by liver steatosis associated with ≥ 1 cardiometabolic criteria and no other identifiable cause [18]. An increasing worldwide mortality burden is a result of the 30% global prevalence of MASLD [1, 26]. MASLD progress to the more aggressive form MASH and promotes fibrosis development in 20% of patients. Liver fibrosis has a major influence on the prognosis of individuals with MASLD because it can result in cirrhosis, hepatocellular carcinoma (HCC), and liver-related mortality [27, 28].

Patients who have NAS ≥ 4 and significant and advanced fibrosis (≥ 2) are regarded as at-risk MASH and they are the focus of pharmacotherapy [29]. Histologic findings from liver biopsies are the most accurate approach for evaluating liver fibrosis. Due to the limitations of liver biopsy, nonintrusive tests for liver fibrosis have been employed [30]. To identify patients at-risk MASH the FAST score was constructed [15]. In the present study we monitored the relation of cardiometabolic criteria to higher FAST score results in Egyptian MASLD patients.

MASLD, MASH, and advanced fibrosis have been demonstrated to be linked to metabolic syndrome [31]. The risks of MASH or severe fibrosis were around three times higher in metabolic syndrome patients [7]. T2DM and obesity are frequently associated with metabolic syndrome [32].

Our research demonstrates the cumulative count of cardiometabolic criteria as the primary factor associated with a high-risk FAST score in Egyptian MASLD patients, with T2DM, dyslipidemia, and hypertension contributing independent, comparable effects. While, obesity had insignificant association with higher FAST score. These results were in concordance with Macedo Silva et al. [33].

The risk of MASH development and HCC is increased when numerous cardiometabolic criteria are present. According to Kanwal et al., the addition of each cardiometabolic criteria increases the risk of MASLD progression up to 2.6 fold in patients with all four criteria [34]. In our study, the presence of all criteria significantly predict higher FAST score.

In the current research, we found that the presence of T2DM significantly predicts increased FAST score. There is a bidirectional relationship between MASLD and T2 DM. Around 60–75% of diabetic patients also have MASLD and the chance for developing T2DM is heightened when MASLD is evident [35, 36].

T2DM contributes to a greater likelihood of liver-related complications and is associated with poor consequences in MASH patients, HCC, and mortality [37]. Therefore, the EASL guidelines recommends to screen for at-risk MASH in T2DM patients [18]. FAST score was presented as an accurate non-intrusive tool to identify T2DM patients and at-risk MASH by Castera et al. [38].

Several risk factors trigger the occurrence of fibrosis and MASH in T2DM. Central obesity and intestinal dysbiosis increase risk of the advancement of steatosis to MASH [39]. Moreover, insulin resistance stimulate lipogenesis in the liver and increases lipid influx resulting in modified mitochondrial function, lipotoxicity, and oxidative stress. Endoplasmic reticulum stress and inflammation cause more hepatic cell injury [40].

In the current investigation, we observed that dyslipidemia had a significant impact on FAST score. As insulin resistance and lipotoxicity alter the lipid profile, elevating triglycerides and LDL cholesterol levels and lowering HDL cholesterol levels [41, 42]. Dyslipidemia was independently linked to risk of progression to fibrosis and cirrhosis [43, 44]. According to Tewari et al., dyslipidemia was directly correlated with FAST score [45].

Hypertension was associated with increased liver fat and MASLD. Hypertension was related to fibrosis progression compared to pre-hypertensive and non-hypertensive patients [46, 47]. In our research, hypertension was significantly linked to a higher FAST score.

Obesity is considered a main element of metabolic syndrome contributing to the development of MASLD. Increased waist circumference denotes abdominal obesity and visceral and liver fat accumulation. Researches by liu et al. and Ghazanfar et al. showed that overweight or obesity are associated with progression of fibrosis, at-risk MASH, cirrhosis, hepatic decompensation and HCC development [48,49,50].

However, our study showed insignificant impact of obesity on higher FAST score agreeing with Malandris et al. and Macedo Silva et al. [33, 51]. The results of Lum et al. also support our findings as they stated that both obese and lean patients have equal risks of MASH and advanced fibrosis. These findings were later explained by the influence of additional comorbidities, such as dyslipidemia and T2DM, that are commonly linked to obesity [52]. This disparity draws attention to possible regional and ethnic variations in MASLD phenotypes.

The Egyptian diet is rich in refined carbohydrates and saturated fats and poor in nutrients, alongside sedentary lifestyles and physical inactivity. These habits contribute to insulin resistance and inflammation, resulting in MASLD progression [53, 54]. Furthermore, MASLD susceptibility and outcomes are greatly influenced by genetic variants and ethnic disparities. However, little is known about the frequency and significance of these variations in the Egyptian population. By altering lipid metabolism and fibrosis risk, a number of genetic variations (such as the PNPLA3 gene) are linked to the severity of MASLD and may have an impact on the variables determining the FAST score [55, 56].

Because metabolic dysfunction frequently precedes severe obesity in high-prevalence areas like Egypt, our work emphasizes the importance of applying risk stratification measures like the FAST score for any patient with multiple metabolic comorbidities.

Conclusion

FAST score is an emerging non-intrusive modality to recognize patients with fibrotic MASH and at an increased chance of acquiring cirrhosis and HCC from fibrosis. Therefore, it is crucial to early recognize these patients. T2DM, hypertension, dyslipidemia and the presence of all cardiometabolic criteria could predict a higher FAST score in patients with MASLD.

Study limitations and recommendations

Our study is limited owing to the comparatively restricted number of patients with high-risk fibrotic MASH (FAST score ≥ 0.67) and the lack of liver biopsy. We recommend screening of at-risk MASH in MASLD patients with T2DM, dyslipidemia, hypertension, and presence of all metabolic criteria by calculating the FAST score.

Data availability

The data that support the findings of this study used are available from the corresponding author on reasonable request.

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Acknowledgements

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Funding

No funds were provided for this research.

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Authors and Affiliations

  1. Tropical Medicine and Infectious Diseases Department, Tanta University, Tanta, Egypt
    Amal Mohammed Dwidar, Ayat Ismail Ferra, Eslam Saber Esmail & Rania Mamdouh Elkafoury
  2. Internal Medicine Department, Tanta University, Tanta, Egypt
    Amany Mohammed Dwidar

Authors

  1. Amal Mohammed Dwidar
  2. Ayat Ismail Ferra
  3. Amany Mohammed Dwidar
  4. Eslam Saber Esmail
  5. Rania Mamdouh Elkafoury

Contributions

Every author participated in the study conception and design. Preparing the materials and data collection were performed by R.E., E.E., and AM.D. Writing the original draft was done by A.I. and A.D. All authors read and approved the final manuscript.

Corresponding authors

Correspondence toAmal Mohammed Dwidar or Rania Mamdouh Elkafoury.

Ethics declarations

Tanta University’s local ethics committee for the medical faculty approved this study (36264PR889/10/24). The study was registered on clinicaltrial.gov with NCT number of NCT06867419. A written informed consent form was filled out by each patient.

Not applicable.

Competing of interests

The authors declare no competing interests.

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Dwidar, A.M., Ferra, A.I., Dwidar, A.M. et al. Metabolic features’ effect on FibroScan-AST (FAST) score in Egyptian patients with metabolic dysfunction-associated steatotic liver disease (MASLD).Egypt Liver Journal 16, 4 (2026). https://doi.org/10.1186/s43066-025-00488-y

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